The idea of F-Consistency is conceptually close to the self-supervised learning, where the unlabeled data is used to pretrain a model using pseudo-labels. In [1]}, the authors described different pretext tasks in medical imaging. Similarly to our approach, the authors of [2]} enforce the model consistency for the initial and augmented images at the prediction and feature levels. Furthermore, the authors of [3]} extended the self-supervised methodology to solve a domain adaptation problem. However, the goal of the self-supervised methods is using a large collection of images without annotations to improve the model's performance on the source domain. Contrary to self-supervised learning, our goal is to achieve the highest possible performance on the target domain.
